Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 'L', 28, 28)
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7fa46371c400>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 'RGB', 28, 28)
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7fa46364c7b8>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    input_real = tf.placeholder(dtype=tf.float32, 
                             shape=(None, image_width, image_height, image_channels),
                             name='input_real')
    input_z = tf.placeholder(dtype=tf.float32, 
                             shape=(None, z_dim), 
                             name='input_z')
    learning_rate = tf.placeholder(dtype=tf.float32, shape=(), name='learning_rate')

    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # Leak factor
    alpha = 0.2
    
    with tf.variable_scope('discriminator', reuse=reuse):   
        x1 = tf.layers.conv2d(images, filters=64, kernel_size=5, strides=2, padding='same')
        relu1 = tf.maximum(alpha* x1, x1)
        
        x2 = tf.layers.conv2d(relu1, filters=128, kernel_size=5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d(relu2, filters=256, kernel_size=5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        dropout = tf.layers.dropout(relu3, rate=0.2)

        flat = tf.reshape(dropout, (-1, 4 * 4 * 256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # Leak factor
    alpha = 0.2
    
    with tf.variable_scope('generator', reuse=not is_train): 
        x1 = tf.layers.dense(z, 7 * 7 * 256)
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        relu1 = tf.maximum(alpha * x1, x1)
                
        x2 = tf.layers.conv2d_transpose(relu1, filters=128, kernel_size=5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        relu2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(relu2, filters=64, kernel_size=5, strides=1, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        relu3 = tf.maximum(alpha * x3, x3)
        
        dropout = tf.layers.dropout(relu3, rate=0.4)
        
        logits = tf.layers.conv2d_transpose(dropout, filters=out_channel_dim, kernel_size=5, strides=2, padding='same')       
        out = tf.tanh(logits)                              
        
        return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    smoothing = 0.2

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_real,
            labels=tf.ones_like(d_model_real) * (1 - smoothing)
        )
    )
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake,
            labels=tf.zeros_like(d_model_fake)
        )
    )
    d_loss = d_loss_real + d_loss_fake

    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake,
            labels=tf.ones_like(d_model_fake)
        )
    )
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    g_vars = [x for x in t_vars if x.name.startswith('generator')]
    d_vars = [x for x in t_vars if x.name.startswith('discriminator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

print_every = 10
show_every = 100

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    _, width, height, channels = data_shape
    input_real, input_z, lr = model_inputs(width, height, channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        
        for epoch in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # Get images and rescale to pass to D
                batch_images = batch_images * 2
                
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                sess.run(d_opt, feed_dict={input_real: batch_images,
                                           input_z: batch_z,
                                           lr: learning_rate})
                sess.run(g_opt, feed_dict={input_real: batch_images,
                                           input_z: batch_z,
                                           lr: learning_rate})
                
                steps += 1
                
                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch + 1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, show_n_images, input_z, channels, data_image_mode)

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 16
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

Epoch 1/2... Discriminator Loss: 1.2561... Generator Loss: 0.7834
Epoch 1/2... Discriminator Loss: 1.2100... Generator Loss: 1.2012
Epoch 1/2... Discriminator Loss: 1.4756... Generator Loss: 3.5894
Epoch 1/2... Discriminator Loss: 1.1350... Generator Loss: 1.1811
Epoch 1/2... Discriminator Loss: 1.7500... Generator Loss: 0.4468
Epoch 1/2... Discriminator Loss: 1.2368... Generator Loss: 1.0447
Epoch 1/2... Discriminator Loss: 1.2499... Generator Loss: 0.9509
Epoch 1/2... Discriminator Loss: 1.4649... Generator Loss: 0.6574
Epoch 1/2... Discriminator Loss: 1.0131... Generator Loss: 1.2303
Epoch 1/2... Discriminator Loss: 1.4297... Generator Loss: 0.6649

Epoch 1/2... Discriminator Loss: 0.9872... Generator Loss: 1.7745
Epoch 1/2... Discriminator Loss: 1.0758... Generator Loss: 1.0959
Epoch 1/2... Discriminator Loss: 1.7663... Generator Loss: 3.0603
Epoch 1/2... Discriminator Loss: 1.3351... Generator Loss: 0.6670
Epoch 1/2... Discriminator Loss: 1.0630... Generator Loss: 1.1553
Epoch 1/2... Discriminator Loss: 1.8681... Generator Loss: 0.3571
Epoch 1/2... Discriminator Loss: 1.0458... Generator Loss: 1.1649
Epoch 1/2... Discriminator Loss: 0.9027... Generator Loss: 1.4220
Epoch 1/2... Discriminator Loss: 0.9770... Generator Loss: 1.2513
Epoch 1/2... Discriminator Loss: 1.2006... Generator Loss: 0.7969

Epoch 1/2... Discriminator Loss: 0.8992... Generator Loss: 1.4126
Epoch 1/2... Discriminator Loss: 0.8566... Generator Loss: 1.5137
Epoch 1/2... Discriminator Loss: 0.9061... Generator Loss: 2.2300
Epoch 1/2... Discriminator Loss: 1.0408... Generator Loss: 1.0740
Epoch 1/2... Discriminator Loss: 0.8708... Generator Loss: 1.5822
Epoch 1/2... Discriminator Loss: 1.6744... Generator Loss: 0.4567
Epoch 1/2... Discriminator Loss: 1.0358... Generator Loss: 1.0546
Epoch 1/2... Discriminator Loss: 0.9015... Generator Loss: 1.5256
Epoch 1/2... Discriminator Loss: 1.1228... Generator Loss: 0.9188
Epoch 1/2... Discriminator Loss: 0.8588... Generator Loss: 1.9153

Epoch 1/2... Discriminator Loss: 0.8845... Generator Loss: 1.4992
Epoch 1/2... Discriminator Loss: 1.0092... Generator Loss: 1.3499
Epoch 1/2... Discriminator Loss: 1.0068... Generator Loss: 1.1574
Epoch 1/2... Discriminator Loss: 0.8492... Generator Loss: 1.5077
Epoch 1/2... Discriminator Loss: 0.9374... Generator Loss: 2.9482
Epoch 1/2... Discriminator Loss: 1.2580... Generator Loss: 0.8091
Epoch 1/2... Discriminator Loss: 0.9110... Generator Loss: 1.2833
Epoch 1/2... Discriminator Loss: 0.7927... Generator Loss: 1.5585
Epoch 1/2... Discriminator Loss: 0.8132... Generator Loss: 1.4413
Epoch 1/2... Discriminator Loss: 1.1653... Generator Loss: 0.9289

Epoch 1/2... Discriminator Loss: 0.9136... Generator Loss: 1.2809
Epoch 1/2... Discriminator Loss: 1.1015... Generator Loss: 0.8742
Epoch 1/2... Discriminator Loss: 1.0222... Generator Loss: 1.0059
Epoch 1/2... Discriminator Loss: 0.7891... Generator Loss: 1.8746
Epoch 1/2... Discriminator Loss: 0.8203... Generator Loss: 2.3413
Epoch 1/2... Discriminator Loss: 0.7869... Generator Loss: 1.8051
Epoch 1/2... Discriminator Loss: 1.0106... Generator Loss: 1.0803
Epoch 1/2... Discriminator Loss: 0.8565... Generator Loss: 1.5013
Epoch 1/2... Discriminator Loss: 0.9238... Generator Loss: 2.9801
Epoch 1/2... Discriminator Loss: 1.2626... Generator Loss: 0.6856

Epoch 1/2... Discriminator Loss: 0.8394... Generator Loss: 1.9434
Epoch 1/2... Discriminator Loss: 0.9788... Generator Loss: 1.1679
Epoch 1/2... Discriminator Loss: 1.1721... Generator Loss: 0.9097
Epoch 1/2... Discriminator Loss: 0.7885... Generator Loss: 1.6250
Epoch 1/2... Discriminator Loss: 0.8152... Generator Loss: 2.2291
Epoch 1/2... Discriminator Loss: 0.8530... Generator Loss: 1.4635
Epoch 1/2... Discriminator Loss: 0.9104... Generator Loss: 1.2504
Epoch 1/2... Discriminator Loss: 1.0736... Generator Loss: 2.0723
Epoch 1/2... Discriminator Loss: 0.7918... Generator Loss: 2.6051
Epoch 1/2... Discriminator Loss: 0.9725... Generator Loss: 1.2400

Epoch 1/2... Discriminator Loss: 1.0230... Generator Loss: 1.0286
Epoch 1/2... Discriminator Loss: 0.9314... Generator Loss: 2.0791
Epoch 1/2... Discriminator Loss: 0.9486... Generator Loss: 1.2993
Epoch 1/2... Discriminator Loss: 1.1986... Generator Loss: 0.8365
Epoch 1/2... Discriminator Loss: 0.8121... Generator Loss: 2.1644
Epoch 1/2... Discriminator Loss: 0.7922... Generator Loss: 2.0818
Epoch 1/2... Discriminator Loss: 1.1618... Generator Loss: 0.8641
Epoch 1/2... Discriminator Loss: 1.0717... Generator Loss: 1.0113
Epoch 1/2... Discriminator Loss: 1.0843... Generator Loss: 0.9073
Epoch 1/2... Discriminator Loss: 0.9392... Generator Loss: 1.1744

Epoch 1/2... Discriminator Loss: 0.9363... Generator Loss: 1.3435
Epoch 1/2... Discriminator Loss: 0.9559... Generator Loss: 1.6013
Epoch 1/2... Discriminator Loss: 0.8809... Generator Loss: 2.1487
Epoch 1/2... Discriminator Loss: 0.8438... Generator Loss: 1.4397
Epoch 1/2... Discriminator Loss: 0.9071... Generator Loss: 1.4686
Epoch 1/2... Discriminator Loss: 1.0012... Generator Loss: 1.3257
Epoch 1/2... Discriminator Loss: 0.9667... Generator Loss: 1.1300
Epoch 1/2... Discriminator Loss: 0.8729... Generator Loss: 2.1002
Epoch 1/2... Discriminator Loss: 0.7732... Generator Loss: 1.9970
Epoch 1/2... Discriminator Loss: 1.3301... Generator Loss: 0.7719

Epoch 1/2... Discriminator Loss: 0.9791... Generator Loss: 1.1511
Epoch 1/2... Discriminator Loss: 0.9534... Generator Loss: 1.1419
Epoch 1/2... Discriminator Loss: 0.9987... Generator Loss: 1.3188
Epoch 1/2... Discriminator Loss: 1.3901... Generator Loss: 0.6340
Epoch 1/2... Discriminator Loss: 1.0429... Generator Loss: 1.3172
Epoch 1/2... Discriminator Loss: 0.9490... Generator Loss: 1.2309
Epoch 1/2... Discriminator Loss: 0.9982... Generator Loss: 1.2056
Epoch 1/2... Discriminator Loss: 0.9243... Generator Loss: 1.4850
Epoch 1/2... Discriminator Loss: 1.3790... Generator Loss: 2.7051
Epoch 1/2... Discriminator Loss: 0.9152... Generator Loss: 1.2787

Epoch 1/2... Discriminator Loss: 1.6037... Generator Loss: 0.5599
Epoch 1/2... Discriminator Loss: 0.9225... Generator Loss: 1.4237
Epoch 1/2... Discriminator Loss: 0.9803... Generator Loss: 1.4341
Epoch 1/2... Discriminator Loss: 0.8672... Generator Loss: 1.3311
Epoch 1/2... Discriminator Loss: 1.1211... Generator Loss: 0.8600
Epoch 1/2... Discriminator Loss: 0.9457... Generator Loss: 1.6562
Epoch 1/2... Discriminator Loss: 1.3943... Generator Loss: 0.6031
Epoch 1/2... Discriminator Loss: 1.0459... Generator Loss: 0.9400
Epoch 1/2... Discriminator Loss: 1.1713... Generator Loss: 0.8541
Epoch 1/2... Discriminator Loss: 1.1710... Generator Loss: 0.7907

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 16
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Epoch 1/1... Discriminator Loss: 1.2015... Generator Loss: 0.9561
Epoch 1/1... Discriminator Loss: 0.6455... Generator Loss: 4.1507
Epoch 1/1... Discriminator Loss: 0.6248... Generator Loss: 4.0548
Epoch 1/1... Discriminator Loss: 0.6612... Generator Loss: 3.4682
Epoch 1/1... Discriminator Loss: 1.0088... Generator Loss: 1.0977
Epoch 1/1... Discriminator Loss: 0.8871... Generator Loss: 2.9800
Epoch 1/1... Discriminator Loss: 1.1812... Generator Loss: 1.0870
Epoch 1/1... Discriminator Loss: 0.7874... Generator Loss: 2.1401
Epoch 1/1... Discriminator Loss: 0.7712... Generator Loss: 1.9402
Epoch 1/1... Discriminator Loss: 1.3821... Generator Loss: 0.7626

Epoch 1/1... Discriminator Loss: 1.8980... Generator Loss: 3.6373
Epoch 1/1... Discriminator Loss: 1.6614... Generator Loss: 0.4617
Epoch 1/1... Discriminator Loss: 1.3871... Generator Loss: 0.7579
Epoch 1/1... Discriminator Loss: 1.1374... Generator Loss: 1.2873
Epoch 1/1... Discriminator Loss: 1.5313... Generator Loss: 0.6296
Epoch 1/1... Discriminator Loss: 1.0040... Generator Loss: 1.3752
Epoch 1/1... Discriminator Loss: 1.2910... Generator Loss: 0.9367
Epoch 1/1... Discriminator Loss: 0.9570... Generator Loss: 2.4776
Epoch 1/1... Discriminator Loss: 1.2766... Generator Loss: 0.7660
Epoch 1/1... Discriminator Loss: 1.2491... Generator Loss: 2.8496

Epoch 1/1... Discriminator Loss: 1.0731... Generator Loss: 1.0682
Epoch 1/1... Discriminator Loss: 0.9611... Generator Loss: 1.8566
Epoch 1/1... Discriminator Loss: 1.0846... Generator Loss: 1.1217
Epoch 1/1... Discriminator Loss: 1.0333... Generator Loss: 1.1676
Epoch 1/1... Discriminator Loss: 1.1564... Generator Loss: 2.1043
Epoch 1/1... Discriminator Loss: 1.1201... Generator Loss: 1.5834
Epoch 1/1... Discriminator Loss: 0.9995... Generator Loss: 1.2873
Epoch 1/1... Discriminator Loss: 1.8800... Generator Loss: 0.4263
Epoch 1/1... Discriminator Loss: 0.9175... Generator Loss: 1.3743
Epoch 1/1... Discriminator Loss: 1.2149... Generator Loss: 0.9340

Epoch 1/1... Discriminator Loss: 1.2274... Generator Loss: 0.8101
Epoch 1/1... Discriminator Loss: 1.0005... Generator Loss: 1.2072
Epoch 1/1... Discriminator Loss: 1.2559... Generator Loss: 0.7488
Epoch 1/1... Discriminator Loss: 0.9887... Generator Loss: 1.6150
Epoch 1/1... Discriminator Loss: 1.0574... Generator Loss: 1.1680
Epoch 1/1... Discriminator Loss: 1.2247... Generator Loss: 0.7488
Epoch 1/1... Discriminator Loss: 1.4509... Generator Loss: 0.6026
Epoch 1/1... Discriminator Loss: 2.2201... Generator Loss: 0.2764
Epoch 1/1... Discriminator Loss: 0.8235... Generator Loss: 2.4130
Epoch 1/1... Discriminator Loss: 1.4645... Generator Loss: 0.5829

Epoch 1/1... Discriminator Loss: 1.2352... Generator Loss: 0.9427
Epoch 1/1... Discriminator Loss: 1.0386... Generator Loss: 1.0983
Epoch 1/1... Discriminator Loss: 1.4018... Generator Loss: 0.6121
Epoch 1/1... Discriminator Loss: 1.4859... Generator Loss: 0.5934
Epoch 1/1... Discriminator Loss: 0.8927... Generator Loss: 1.5142
Epoch 1/1... Discriminator Loss: 1.0152... Generator Loss: 1.9840
Epoch 1/1... Discriminator Loss: 1.6807... Generator Loss: 0.4777
Epoch 1/1... Discriminator Loss: 0.9816... Generator Loss: 1.2489
Epoch 1/1... Discriminator Loss: 1.5622... Generator Loss: 0.6283
Epoch 1/1... Discriminator Loss: 1.3719... Generator Loss: 0.7006

Epoch 1/1... Discriminator Loss: 1.1010... Generator Loss: 1.0374
Epoch 1/1... Discriminator Loss: 1.1439... Generator Loss: 0.9799
Epoch 1/1... Discriminator Loss: 1.2896... Generator Loss: 0.7241
Epoch 1/1... Discriminator Loss: 1.2222... Generator Loss: 0.9271
Epoch 1/1... Discriminator Loss: 0.9999... Generator Loss: 1.5031
Epoch 1/1... Discriminator Loss: 1.2630... Generator Loss: 0.8918
Epoch 1/1... Discriminator Loss: 1.0874... Generator Loss: 1.0421
Epoch 1/1... Discriminator Loss: 1.1851... Generator Loss: 1.9537
Epoch 1/1... Discriminator Loss: 1.1695... Generator Loss: 0.9736
Epoch 1/1... Discriminator Loss: 1.7942... Generator Loss: 0.4591

Epoch 1/1... Discriminator Loss: 1.1231... Generator Loss: 1.0319
Epoch 1/1... Discriminator Loss: 1.5018... Generator Loss: 0.5240
Epoch 1/1... Discriminator Loss: 1.1425... Generator Loss: 1.0537
Epoch 1/1... Discriminator Loss: 1.3417... Generator Loss: 0.9338
Epoch 1/1... Discriminator Loss: 1.1660... Generator Loss: 0.9875
Epoch 1/1... Discriminator Loss: 1.1353... Generator Loss: 1.0295
Epoch 1/1... Discriminator Loss: 1.1107... Generator Loss: 0.9780
Epoch 1/1... Discriminator Loss: 0.9855... Generator Loss: 1.3734
Epoch 1/1... Discriminator Loss: 1.0530... Generator Loss: 1.2026
Epoch 1/1... Discriminator Loss: 0.9607... Generator Loss: 1.3043

Epoch 1/1... Discriminator Loss: 1.0030... Generator Loss: 1.2149
Epoch 1/1... Discriminator Loss: 1.3085... Generator Loss: 1.3376
Epoch 1/1... Discriminator Loss: 1.1425... Generator Loss: 0.9719
Epoch 1/1... Discriminator Loss: 1.2164... Generator Loss: 1.6502
Epoch 1/1... Discriminator Loss: 1.0756... Generator Loss: 1.8235
Epoch 1/1... Discriminator Loss: 1.2474... Generator Loss: 1.1080
Epoch 1/1... Discriminator Loss: 1.2655... Generator Loss: 1.0927
Epoch 1/1... Discriminator Loss: 1.2743... Generator Loss: 0.8363
Epoch 1/1... Discriminator Loss: 1.4530... Generator Loss: 0.5928
Epoch 1/1... Discriminator Loss: 1.0507... Generator Loss: 1.8135

Epoch 1/1... Discriminator Loss: 1.1995... Generator Loss: 0.8907
Epoch 1/1... Discriminator Loss: 1.2027... Generator Loss: 0.8201
Epoch 1/1... Discriminator Loss: 1.2593... Generator Loss: 0.8879
Epoch 1/1... Discriminator Loss: 1.2661... Generator Loss: 0.7706
Epoch 1/1... Discriminator Loss: 1.0851... Generator Loss: 1.5262
Epoch 1/1... Discriminator Loss: 1.3325... Generator Loss: 0.7759
Epoch 1/1... Discriminator Loss: 1.2955... Generator Loss: 0.8211
Epoch 1/1... Discriminator Loss: 1.1680... Generator Loss: 0.8798
Epoch 1/1... Discriminator Loss: 1.1532... Generator Loss: 0.9523
Epoch 1/1... Discriminator Loss: 1.3586... Generator Loss: 0.6430

Epoch 1/1... Discriminator Loss: 1.4601... Generator Loss: 0.6701
Epoch 1/1... Discriminator Loss: 1.1887... Generator Loss: 0.9432
Epoch 1/1... Discriminator Loss: 1.0504... Generator Loss: 1.2223
Epoch 1/1... Discriminator Loss: 1.0433... Generator Loss: 1.3734
Epoch 1/1... Discriminator Loss: 1.3118... Generator Loss: 0.6994
Epoch 1/1... Discriminator Loss: 1.0775... Generator Loss: 1.0387
Epoch 1/1... Discriminator Loss: 1.3501... Generator Loss: 2.2140
Epoch 1/1... Discriminator Loss: 1.1650... Generator Loss: 0.8591
Epoch 1/1... Discriminator Loss: 1.4787... Generator Loss: 0.5662
Epoch 1/1... Discriminator Loss: 1.1274... Generator Loss: 1.0594

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.